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Machine Learning Engineer Jobs in California, MD

ManTech

Senior Systems Engineer

Lexington Park, MD

$150K - $268K/yr

Needs to occasionally move about inside the office to access file cabinets, office machinery, etc ... Learning and Development opportunities, wellness programs as well as other optional benefit ...

DataRobot

Lead Engagement Director

California, MD

$180K - $220K/yr

Experience managing data science or machine learning projects Education: * Technical undergraduate degree (Computer Science, Engineering, or similar); MBA preferred Certifications: * PMP, Scrum ...

DataRobot

Lead Engagement Director

California, MD

$180K - $220K/yr

Experience managing data science or machine learning projects Education: * Technical undergraduate degree (Computer Science, Engineering, or similar); MBA preferred Certifications: * PMP, Scrum ...

Cloudera

Sr. Staff Engineer

California, MD · Remote

$213K - $266K/yr

Engineering Seniority Level: Mid-Senior level At Cloudera, we empower people to transform complex ... Solid knowledge of Linux environments, networking, virtual machines and container technologies (e.g ...

Astrion

Software Engineer - Midlevel

Dahlgren, VA · On-site

$120K - $140K/yr

Overview Software Engineer - Midlevel Location: Dahlgren, Virginia Status: Full Time Clearance ... ones while learning and utilizing the latest technologies. The candidate must be a proven team ...

Astrion

Software Engineer - Midlevel

Dahlgren, VA · On-site

$120K - $140K/yr

Overview Software Engineer - Midlevel Location: Dahlgren, Virginia Status: Full Time Clearance ... ones while learning and utilizing the latest technologies. The candidate must be a proven team ...

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Showing results 1-20

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in California, MD

Machine Learning Engineer information

See California, MD salary details

$30.2K

$123.4K

$185.4K

How much do machine learning engineer jobs pay per year?

As of Jul 4, 2026, the average yearly pay for machine learning engineer in California, MD is $123,398.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,300.00 and $148,500.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What cities near California, MD are hiring for Machine Learning Engineer jobs? Cities near California, MD with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Junior Data Scientist with Security Clearance

Junior Data Scientist with Security Clearance

Forge Forward Inc.

Lexington Park, MD • On-site

Other

Posted 25 days ago

Job description

Forge Forward is searching for a Junior Data Scientist to support a client in Lexington Park, MD. This position will require at least 4 days a week on-site. Applicants must live within driving distance to be considered. This position requires 0–2 years of experience and is a technical role focused on data engineering, analysis, and visualization in support of PMA‐213, contributing to Forge Forward’s digital transformation efforts. The role supports the development and sustainment of data pipelines, integration of disparate data sources, and creation of dashboards and analytics tools that enable mission-focused decision making across Integrated Product Teams. The candidate will support the scoping and implementation of technical solutions to address data, system, and integration challenges, while applying expertise in data analysis, visualization, and analytics development. Responsibilities include evaluating data requirements, structuring and standardizing data, and ensuring data quality, consistency, and reliability across systems. The role also involves developing scalable data models and frameworks that support software development, dashboards, and decision-support capabilities. Functional Responsibilities and Required Skills:
• Gather, assess, and structure structured and unstructured data to support analysis and development of data products
• Collaborate with team members and stakeholders to collect, validate, and analyze data from multiple sources
• Support development and maintenance of data pipelines, datasets, and analytical outputs
• Process and transform data to support dashboards, reporting, and decision-support tools
• Develop and maintain data models and structures that enable visualization, analytics, and software development use cases
• Perform basic data quality checks, validation, and standardization to ensure consistency and reliability
• Utilize SQL and/or scripting (e.g., Python) to query, manipulate, and analyze data Required Skills:
• US citizen with Secret clearance
• B.S. in Data Science or related field Desired Skills:
• Familiarity with querying relational databases using SQL
• Familiarity with Python, R, or similar scripting/programming languages
• Experience using Business Intelligence tools (Power BI, Tableau, Qlik, or similar) to develop dashboards and visualizations
• Exposure to data integration, transformation, and structuring concepts (joins, unions, data shaping, etc.)
• Understanding of data modeling concepts (tables, relationships, basic schemas)
• Exposure to working with multiple data sources and integrating data sets
• Familiarity with version control or collaborative development environments
• Interest in applying data to operational, logistics, or readiness-related use cases
• Exposure to machine learning or optimization concepts (academic or project-based experience is sufficient)
• General awareness of emerging tools such as Generative AI and their application to data workflows

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